Search

In this study, a stability analysis and H ∞ performance are derived for the bilateral teleoperation systems with time-varying delay. A new model transformation is firstly applied by employing a three-term approximation for the delayed state with the scaled small-gain theorem (SSG). By using appropriate Lyapunov–Krasovskii functional (LKF) and a generalized free-matrix-based integral inequality, new stability analysis and H ∞ performance criteria are proposed in terms of linear matrix inequalities. Finally, the engineering relevance of theory has been highlighted with different examples. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of delay-dependent robust filtering design for polytopic systems with a time-varying delay. A new model transformation is firstly applied by employing a three-term approximation for the delayed state, which leads to a smaller approximation error than the two-term approximation. Then, based on the scaled small-gain Theorem combined with an appropriate Lyapunov-Krasovskii Functional, the performance analysis of the filtering error system is examined and then the full- and reduced-order filters are designed in terms of linear matrix inequalities via a simple linearisation technique. Before the end, a sufficient condition is presented to solve the problem of filtering design for a time-delay system without polytopic uncertainties. Finally, illustrative examples are presented to demonstrate the validity of the proposed methods. [ABSTRACT FROM AUTHOR]

This brief investigates the problem of mean square exponential stability of uncertain stochastic delayed neural networks (DNNs) with time-varying delay. A novel Lyapunov functional is introduced with the idea of the discretized Lyapunov--Krasovskii functional (LKF) method. Then, a new delay-dependent mean square exponential stability criterion is derived by applying the free-weighting matrix technique and by equivalently eliminating time-varying delay through the idea of convex combination. Numerical examples illustrate the effectiveness of the proposed method and the improvement over some existing methods. [ABSTRACT FROM AUTHOR]

This paper considers the problem of delay-dependent exponential stability in mean square for stochastic systems with polytopic-type uncertainties and time-varying delay. Applying the descriptor model transformation and introducing free weighting matrices, a new type of Lyapunov-Krasovskii functional is constructed based on linear matrix inequalities (LMIs), and some new delay-dependent criteria are obtained. These criteria include the delay-independent/ratedependent and delay-dependent/rate-independent exponential stability criteria. These new criteria are less conservative than existing ones. Numerical examples demonstrate that these new criteria are effective and are an improvement over existing ones. [ABSTRACT FROM AUTHOR]

Abstract: This paper is concerned with the problem of global exponential stability analysis for a class of cellular neural networks with time-varying discrete and distributed delays (DDCNNs). A new delay-dependent sufficient condition is derived for the global exponential stability of the DDCNNs by using the integral inequality method and the newly proposed Lyapunov–Krasovskii functional. The obtained stability condition is less conservative than some of the existing results in the literature. Numerical examples are given to demonstrate the effectiveness and superiority of the proposed results. [Copyright &y& Elsevier]

Abstract: In this paper, the mean square exponential stability is investigated for a class of discrete-time stochastic neural networks with time-varying delays and norm-bounded uncertainties. Based on Lyapunov stability theory and stochastic approaches, delay-dependent criteria are derived to ensure the robust exponential stability in the mean square for the addressed system. Meantime, by using the numerically efficient Matlab LMI Toolbox, a example is presented to show the usefulness of the derived LMI-based stability condition. [Copyright &y& Elsevier]

This brief is concerned with asymptotic stability of neural networks with uncertain delays. Two types of uncertain delays are considered: one is constant while the other is time varying. The discretized Lya- punov-Krasovskii functional (LKF) method is integrated with the technique of introducing the free-weighting matrix between the terms of the Leibniz-Newton formula. The integrated method leads to the establishment of new delay-dependent sufficient conditions in form of linear matrix inequalities for asymptotic stability of delayed neural networks (DNNs). A numerical simulation study is conducted to demonstrate the obtained theoretical results, which shows their less conservatism than the existing stability criteria. [ABSTRACT FROM AUTHOR]

Abstract-The robust state estimation problem for a class of uncertain neural networks with time-varying delay is studied in this paper. The parameter uncertainties are assumed to be norm bounded. Based on a new bounding technique, a sufficient condition is presented to guarantee the existence of the desired state estimator for the uncertain delayed neural networks. The criterion is dependent on the size of the time-varying delay and on the size of the time derivative of the time-varying delay. It. is shown that the design of the robust state estimator for such neural networks can be achieved by solving a linear matrix inequality (LMI), which can be easily facilitated by using some standard numerical packages. Finally, two simulation examples are given to demonstrate the effectiveness of the developed approach. [ABSTRACT FROM AUTHOR]

Abstract: This Letter concerns with the mean square exponential stability of uncertain stochastic delayed neural networks. By applying Lyapunov functional method, new delay-dependent/independent mean square exponential stability criteria are derived in terms of linear matrix inequalities. Two examples are presented which show our result are less conservative than the existing stability criteria. [Copyright &y& Elsevier]

In this letter, the global asymptotical stability analysis problem is considered for a class of Markovian jumping stochastic Cohen-Grossberg neural networks (CGNNs) with mixed delays including discrete delays and distributed delays. An alternative delay-dependent stability analysis result is established based on the linear matrix inequality (LMI) technique, which can easily be checked by utilizing the numerically efficient Matlab LMI toolbox. Neither system transformation nor free-weight matrix via Newton-Leibniz formula is required. Two numerical examples are included to show the effectiveness of the result. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we consider the problems of robust stability, robust stabilization and H ∞ control via memoryless state feedback for uncertain discrete-time switched systems with mode-dependent time delays. Based on linear matrix inequalities (LMIs), a descriptor model transformation of the system and a switched Lyapunov function, new delay-dependent criteria are established which are not contained in known literatures. Numerical examples are presented to illustrate the effectiveness of the theoretical results. [Copyright &y& Elsevier]

Abstract: In this paper, we consider the problems of robust stability and stabilization via memoryless state feedback for uncertain discrete-time switched systems with mode-dependent time-varying delays. By using a descriptor system method and linear matrix inequality technique, and by introducing a switched Lyapunov functional, we establish some new delay-dependent stability and stabilization criteria for the system. Numerical examples are presented to illustrate the effectiveness of the theoretical results. [Copyright &y& Elsevier]

This brief focuses on the problem of delay-dependent stability analysis of neural networks with variable delay. Two types of variable delay are considered: one is differentiable and has bounded derivative; the other one is continuous and may vary very fast. By introducing a new type of Lyapunov-Krasovskii functional, new delay-dependent sufficient conditions for exponential stability of delayed neural networks are derived in terms of linear matrix inequalities. We also obtain delay-independent stability criteria. Two examples are presented which show our results are less conservative than the existing stability criteria. [ABSTRACT FROM AUTHOR]

This paper considers the problems of stability, stabilization and H∞ control via memoryless state feedback for uncertain discrete-time Markovian jump linear systems with mode-dependent time delays. Based on linear matrix inequalities, delay-dependent solutions are obtained by using a descriptor model transformation of the system and by applying a new bounding technique for cross terms. Numerical examples are presented to illustrate the usefulness of the theoretical results. [Copyright &y& Elsevier]